Music similarity is a relatively new topic, and at this moment, the interest into it is quite academic. Systems have been developed that compare music pieces with one another using statistics over what is called ‘timbre’—a mixture of a variety of low-level features. Various distance measures have been proposed including expensive methods like Monte-Carlo-simulation of samples of a distribution and probability estimation of the artificial samples using the statistics from the other music piece. See e.g. [3] for details.
The state of the art in emotion recognition in music is a rather new topic. While a huge amount of papers have been written about music processing in general, few papers have been published regarding emotion in music. State of the art system used for emotion classification in music classifiers include Gaussian mixtures models, support vector machines, neural networks etc.
There are also studies about perception of emotion in music, but the results are still very preliminary. Reference [1] and [2] provides information about the state-of-the art mood detection techniques.